On Discrete Least Squares Polynomial Fit, Linear Spaces and Data Classification
Abstract
The best discrete least squares polynomial fit to a data set is revisited. We point out some properties related to the best polynomial and precise the dimension of vector spaces encountered to solve the problem. Finally, we suggest a basic classification of data sets based on their increasing or decreasing trend, and on their convexity or concavity form.
DOI: https://doi.org/10.3844/jmssp.2007.222.227
Copyright: © 2007 François Dubeau and Youness Mir. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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Keywords
- Polynomial data fitting
- weighted least squares
- orthogonal polynomials
- linear spaces
- data classification